Genes involved in asymmetric cell division

ABSTRACT

The present invention relates to a method to isolate genes involved in the process of asymmetric cell division. The invention relates further to genes isolated using this method, and their use in controlling root formation, preferably lateral root formation.

RELATED APPLICATIONS

This application is a national stage application (under 35 U.S.C. 371) of PCT/EP2006/065739 filed Aug. 28,2006, which claims benefit of European application 05107830.1 filed Aug. 26, 2005.

The present invention relates to a method to isolate genes involved in the process of asymmetric cell division. The invention relates further to genes isolated using this method, and their use in controlling root formation, preferably lateral root formation.

To generate the multitude of different cell types present in multicellular organisms, cell divisions resulting in daughter cells with different fates are vital and decisive in various developmental processes (Scheres & Benfey, 1999). This type of divisions is called asymmetric, whether or not asymmetry is morphologically visible at the time of division (Horvitz & Herskowitz, 1992). Especially in plants, where cell movement is limited, the control of the cell division plane has traditionally been considered important for the formation of regular patterns, i.e. correct divisions during embryogenesis or stomata formation, the formation of ordered cell files in the meristem. (Scheres & Benfey, 1999).

During a plant life cycle, several asymmetric divisions occur: (a) the first division of the zygote (Mansfield & Briarty, 1991); (b) the embryonic division that gives rise to the lens-shaped progenitor cell of the quiescent centre (Dolan et al., 1993); (c) the male microspore division (Twell et al., 1998); (d) divisions during stomatal complex formation (Larkin et al., 1997); (e) oriented periclinal divisions in the early embryo that separate the progenitor cells for the three main tissues, epidermis, ground tissue, and vascular tissue (Jurgens & Mayer, 1994); (f) stem cell divisions that separate differentiation-competent daughter cells and new stem cells in the root (Dolan et al., 1993; van den Berg et al., 1995); and (g) also during lateral root initiation (Casimiro et al., 2003).

Asymmetric divisions fundamentally differ from the standard proliferative divisions in their limited spatio-temporal way of occurrence. Furthermore the number of cells involved is minimal. These characteristics make it difficult to analyze (genome wide) transcript expression during this process.

Up till now only few transcript profiling experiments have been performed, in various organisms, on processes where asymmetric cell divisions are involved, i.e. during gliogenesis in Drosophila (Egger et al., 2002), Arabidopsis pollen development (Honys & Twell, 2003, 2004; Becker et al, 2003) and lateral root initiation (Himanen et al., 2004). However, none of these approaches aimed at or resulted in the identification of the genetic pathway driving the asymmetric division itself.

In the case of lateral root initiation a few pericycle cells divide anticlinally and asymmetrically (Casero et al., 1993). This is not a continuous process and is exposed to various environmental cues and endogenous signals. Furthermore, these divisions only occur in those pericycle cell files that are in close proximity to the xylem pole (Casimiro et al., 2003).

Micro-array approaches have revealed a broader view on auxin signaling towards LRI (Himanen et al., 2004). For these analyses, a lateral root inducible system was used. In this system, auxin transport, signaling and the G1-to-S cell cycle transition are blocked in seedlings growing on medium supplemented with NPA. Subsequently, these seedlings are transferred to medium containing auxin (NAA) for 1-12 hours. This allowed an inducible start up of auxin signaling and progression through the G1-to-S transition (Himanen et al., 2002).

An adaptation of this lateral root inducible system can also be used for the study of asymmetric cell divisions. We present a unique approach that allowed us to circumvent problems like tissue specificity and the limited number of cells involved through isolating specifically asymmetrically dividing pericycle cells at the xylem pole during LRI. Therefore we combined 4 strategies: 1) a recently developed lateral root inducible system, that synchronously induces the asymmetric divisions during LRI (Himanen et al., 2002), 2) a xylem pole pericycle specific GFP marker line (J0121), 3) a Fluorescent Assisted Cell Sorting approach (Birnbaum et al., 2003), and 4) genome-wide micro array analysis on the isolated xylem pole pericycle cells. This combined strategy allowed us not only to identify those genes involved directly in the LRI process but also to extrapolate the results to the general concept of asymmetric division. We found, as potential regulators of asymmetric divisions, genes involved in cell cycle regulation and a high percentage of genes associated with cytoskeleton organization and dynamics.

It is a first aspect of the invention to provide a method to isolate genes involved in asymmetric cell division, comprising: (1) subjecting roots of a wild type plant to a treatment inducing lateral root initiation in a synchronous way; (2) subjecting roots of a mutant not developing lateral roots by a defect in auxin signalling to a treatment inducing lateral root initiation in wild type in a synchronous way; (3) identifying genes that are induced in wild type but not in mutant; (4) identifying genes induced in the xylem pole pericycle in wild type during lateral root initiation. Early lateral root initiation as used here means the events at different stages just prior to the first division in the pericycle. Preferably this is within 10 hours after auxin induction of the lateral root, more preferably within 8 hours of said induction, even more preferably 6 hours after said induction. Preferably, the mutant used is a slr-1 mutant.

Preferably, said method is further comprising the use of a xylem pole pericycle marker line, followed by cell sorting. Even more preferably, said marker is GFP. Most preferably, the isolated cells are genome wide analysed by microarray analysis

Another aspect of the invention is a gene involved in early lateral root formation, isolated with the method according to the invention. Preferably, said gene encodes a transcription factor. Even more preferably, said transcription factor is selected from the group consisting of SEQ ID No 1 to SEQ ID No 19.

As transcription factors often are expressed in the tissue of activity, their promoters are likely to be of economical use. Such promoters can be used in several strategies to enhance pathogen tolerance/resistance of the plant.

Another aspect of the invention is a gene involved in asymmetric cell division, isolated with the method according to the invention. Preferably said method is the method further comprising the use of a xylem pole pericycle marker line, followed by cell sorting. Even more preferably, said gene is comprising a sequence encoding a protein selected from the group consisting of SEQ ID No 20-SEQ ID No 34, or a homologue thereof.

Still another aspect of the invention is a transcription factor involved in early lateral root formation, whereby said transcription factor is selected from the group consisting of SEQ ID No 1 to SEQ ID No 19. Preferably, the gene encoding said transcription factor is isolated with the method of to the invention.

A further aspect of the invention is the use of a gene, isolated with the method of the invention, to modulate early lateral root initiation. Modulation as used here may be an increase or a decrease in number of lateral roots, it may be an increase or decrease in size of the lateral roots or it may be a shift in time (earlier or later in plant development) of lateral root formation. Preferably, said modulation is an increase or decrease in lateral roots, even more preferably it is an increase in lateral roots. Preferably, said gene is encoding a transcription factor. Even more preferably, said gene is encoding a transcription factor selected from the group consisting of SEQ ID No 1 to SEQ ID No 34, or a homologue thereof. Said genes may be used in combination to increase the effect on lateral root formation

Gene as used here refers both to the genomic sequence (including possible introns) as well as to the cDNA derived from the spliced messenger. It may refer to the promoter sequence too. However, it is clear for the person skilled in the art that for some applications, the coding sequence, such as it may be derived from the cDNA, may be operably linked to a suitable promoter. Operably linked refers to a juxtaposition wherein the components so described are in a relationship permitting them to function in their intended manner. A promoter sequence “operably linked” to a coding sequence is ligated in such a way that expression of the coding sequence is achieved under conditions compatible with the promoter sequence.

A homologue as used here means that the protein encoded by the gene has an amino acid sequence that is at least 75% identical, and even more preferably at least 80% identical, and even more preferably at least 85% identical, and even more preferably at least 90% identical and even more preferably at least 95% identical, and even more preferably at least 96% identical, and even more preferably at least 97% identical, and even more preferably at least 98% identical, and even more preferably at least 99% identical, as measured by a BLASTP search (Altschul et al., 1997)

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1: Lateral root inducible system (A-G) Time-series of 12 h of NM treatment (blue staining=P::GUS reporter activity of Arath;CYCB1;1 marking G2/M transition) The auxin transport inhibition blocks all lateral root initiation, allowing synchronous induction of lateral roots via auxin, resulting in the onset of G2/M transition 6 to 8 h after transfer to auxin medium (D,E)

FIG. 2: Scheme of main events during lateral root initiation

FIG. 3: Figure Of Merit calculation for a range to 20 clusters

FIG. 4: Cross-table representation with the frequencies of all combinations of expression profiles

FIG. 5: Overexpression phenotype of CYCD3;1, E2Fa/DPa and CDKB1;1 in 10 day old seedlings, as compared to wild type

FIG. 6: Xylem pole pericycle specific expression of a stabilised mutant version of the BDL protein in J0121xUAS:bdl (0.0±0.0), resulting in a lateral rootless phenotype, while the control lines Col-0 (3.0±0.1), J0121 (4.1±0.1), UAS:bdl (3.1±0.1), UAS:BDL (2.8±0.1) and J0121xUAS:BDL (3.6±0.1) display no reduction in the number of lateral roots/cm

FIG. 7: Combined mutations in various members of the CYCA2;4 family results in dramatic reductions of lateral root density (left panel: Col-0 control; right panel: multiple mutant)

FIG. 8: Analysis of the lateral root phenotype of several homozygous SALK T-DNA insertion mutants, derived from genes in various clusters. The code of the mutants is listed in Table 5.

FIG. 9: Detailed analysis during early lateral root formation (asymmetric cell division is indicated by arrowheads) of various up and down regulated genes in the complete dataset. The numbers refer to the fusions listed in Table 6.

EXAMPLES

Materials & Methods to the Examples

WT Col—Slr Comparison Based Approach

Sampling

In the lateral root inducible system seeds (Col-0 & slr-1) were germinated on standard Murashige & Skoog media containing 10 μM N-NaphtylPhtalamic Acid (NPA) on vertically oriented square plates (Greiner Labortechnik, Frickenhausen, Germany) in a growth chamber under continuous light (110 μE·m−2·s−1 PAR supplied by cool-white fluorescent tungsten tubes [Osram, München, Germany]) at 22° C. (Himanen et al., 2002). 72 h after germination (0 h time-point), only those seedlings (wild-type & mutant) that made full contact with the medium were transferred to medium containing 10 μM NaphtylAcetic Acid (NAA) and harvested after 2 h and 6 h. These 3 time-points were applied for both wild-type and mutant. A mock treatment was included for wild-type only, by transferring the seedlings to standard Murashige & Skoog medium without NAA addition. For all time-points only the lateral root inducible segments were used for the analysis. For this purpose the root apical meristem and hypocotyl were manually removed to minimise contamination with other cell types. All treatments were repeated.

Microarray and Clustering

RNA was extracted using the RNeasy Minikit (Qiagen). RNA quality and quantity were analysed using RNA 6000 Nano Lab Chip Kit (Agilent Technologies, Germany). For microarray 5.8 μg total RNA was used. Double stranded cDNA was synthesized with Life Technologies cDNA Synthesis Kit. The double stranded cDNA was converted to biotin-labelled cRNA (Ambion MEGA script T7 in vitro transcription kit and biotin containing ribonucleotides from Enzo (LOXO GmbH)). 15 μg of fragmented cRNA was used for hybridization to ATH1 Affymetrix® gene-chips. The biotin-labelled RNA was visualised with phycoerythrin-streptavidin labels. ATH1 gene-chips (Affymetrix) represent 22747 Arabidopsis genes (˜85% predicted genes in the Arabidopsis genome).

The overall signal of the different chips was normalized using Microarray Suite 5.0 software (Affymetrix). The raw data were exponentially distributed and were therefore ₂log-transformed before further statistics. Statistical significance was analysed via ANalysis Of VAriance (ANOVA) for every gene. This resulted in a p-value for three sources of variance: the effect of the time-course, the effect of the genotype and the effect of their interaction. For the genome-wide transcript profiling the stringency was increased to p<0.001. This is the equivalent of 23 false positive tests if 22747 tests are performed. At this level of significance 3110 genes were flagged.

As we need to detect differences between the expression profiles in both genotypes we needed a tool to optimally visualise these differences. We obtained this tool by merging time-course data for both genotypes per time-point. This merged dataset was subsequently treated as if it was a single time-course (repeated time-points are indicated with *) (0/0*/2/2*/6/6*). Before clustering, an estimate of the predictive power of a clustering algorithm (Figure Of Merit) was computed over a range of clusters. The lower the Figure Of Merit, the higher the predictive power of the clustering will be (Yeung et al., 2001). The number of clusters, for which the smallest increment did not result in a decrease of the Figure Of Merit, was chosen as the optimal cluster number. All clustering computation was performed using TIGR Multi-experiment Viewer 2.2 (tigr.org webpage, Oct. 11, 2003).

Each gene was related to two clusters, representing its average expression profile in both wild-type and mutant. The combinational potential was represented in a cross-table format with indication of the frequency of occurrence of each combination. As an indicator of differences between clusters, a colour code was applied. For all clusters, the relative induction/reduction rates of the expression profiles between 0 h and 2 h and between 0 h and 6 h of the average profiles were compared to one another. If these relative induction/reduction rates differed 2-fold or more at one of these levels of comparison an orange or blue colour was assigned to this cluster combination. If these relative induction/reduction rates differed at both levels 2-fold or more, a red colour was assigned to this cluster combination. A cluster was considered as up-regulated when the rate of induction of the expression level was stronger than 2-fold for both intervals (0-2 and 0-6). Only clusters 1, 2, 3 and 4 met these criteria.

Cell Sorting Approach

Sampling

In the lateral root inducible system seeds (J0121, plantsci.cam.ac.uk/Haseloff/geneControl/cataloques/Jlines/record/record_(—)0.html webpage) were germinated (Himanen et al., 2002). As described above, seedlings were harvested after 2 h and 6 h. For all time points the roots were cut into small 0.5 mm fragments, and those segments were protoplasted according to Birnbaum et al. (2003, 2005). GFP expressing cells were isolated on a fluorescence activated cell sorter (Becton Dickinson FACSVantage). The cells were sorted directly into lysis buffer (Qiagen RLT buffer), mixed and immediately frozen at −80° C. for later RNA extraction. All treatments were repeated.

Microarray and Clustering

Standard Affymetrix protocols for small samples were then used for amplifying, labeling and hybridizing RNA samples (wi.mit.edu/CMT/protocols/AffySmlSamplProto.pdf webpage). Then hybridized cRNA was fragmented as described in the GeneChip® Expression Analysis Technical Manual. The hybridization, washing and staining steps were performed according to the Affymetric protocols (wi.mit.edu/CMT/protocols/Affymetrix%20User%20Manual.pdf webpage).

The data were processed using a Mixed Model. This mixed-model analysis of variance was performed to identify genes differentially expressed between the various treatments (Chu et al., 2002, 2004). In this approach, a global normalization step was applied to minimize general array-level effects by centering the mean of the log₂-transformed values to zero for each array (Chu et al., 2002). Outlier probes with values greater than two standard deviations from the probe-set mean were then removed. Next, a mixed-model ANOVA was applied to the transformed and centered intensity values obtained from the global normalization step. This gene model, which is based on that developed by Chu et al. (2002), can be formalized as: log₂(PM _(jkl))=T _(j) +P _(k) +A _(l(j))+ε_(jkl) where the PM variable refers to the output of the global normalization procedure for each gene, as described above. The symbols T, P, and A represent treatment, probe, and array effects, respectively. The array effect A_(l(j)) is assumed to be a normally distributed random effect (Chu et al., 2002). A standard error term ε_(jkl) was also applied to this model. In addition, the indices j, k, and/represent the jth treatment, on the kth probe, and on the lth replicate (Chu et al., 2002). The output of this model is the mean expression value for every gene, based on the global model, as well as a p-value from the gene-model for the probability of falsely rejecting the null hypothesis of no-differential expression (α=0.05). The global and gene models were run on a Linux server with the statistical software SAS (version 8.2).

Grouping of the 1920 significantly differentially expressed genes coming out of the statistical analysis into 10 clusters, was done using TIGR MeV 3.0.3 (Saeed et al., 2003).

Example 1 Sampling and Microarray Analysis

Recently, we developed an auxin-based lateral root inducible system (Himanen et al., 2002). Based on this unique, in planta inducible system we performed a genome-wide transcript profiling, to identify key regulators of lateral root initiation. To facilitate the identification of those genes with a role in auxin signalling in relation to lateral root initiation a mutant was included as a negative control. The mutant (solitary root) was mainly selected for its inability to form lateral roots and because the affected gene is involved in a known part of auxin signalling (Fukaki et al., 2002). The comparison of wild-type and mutant in the lateral root inducible system is of fundamental importance to select genes involved in lateral root initiation, downstream of the protein affected in the mutant (IAA14/SLR).

Time-points were chosen in such a way that we could monitor gene expression at different stages just prior to the first division in the pericycle. Himanen et al. (2002) showed that this event occurs 8 to 10 h after transfer to auxin containing medium. The zero time-point (72 h NPA) is consistent with a G1/S-blocked state, while 6 h after transfer to auxin pericycle cells adjacent to the xylem poles are nearly starting G2/M transition. Furthermore, the earliest auxin response in the root was visualised with a DR5::GUS reported 1.5 to 2 h after auxin treatment (FIG. 2). Therefore, a time-point (2 h NAA) was included to represent this earliest auxin-modulated transcription.

Both wild-type and mutant (slr-1) were subjected to these treatments. Additionally, a mock-treatment was included for wild type to assess differential gene expression due to the transfer. All treatments were biologically repeated adding to the statistical significance of the data.

Example 2 Statistical Analysis and Clustering

After normalisation and transformation, the data were subjected to ANOVA analysis. Comparison of the previous limited transcript profiling (on 4600 genes) (Himanen et al., unpublished results) with the present one, clearly shows that our lateral root inducible system is highly reproducible, since 64% of the differentially expressed genes were confirmed when checked at the same level of significance (p<0.005). In order to reduce the amount of false positives even further, we applied a 5-fold higher stringency (p<0.001) than in the previous transcript profiling. At this high stringency level still 3110 genes were differentially regulated. Clustering of all data-points for both wild-type and solitary root separately did not meet our needs to assess the differences in expression profiles in both genotypes. In order to meet this criterion, the data for both genotypes were combined in one dataset. Each gene was represented twice in the combined dataset, resulting in 6220 expression profiles. In order to estimate the optimal number of clusters, the Figure Of Merit (FOM) was computed for a range of clusters. The smallest FOM was estimated at 14 clusters, representing the optimal number of clusters corresponding to the highest predictive power (FIG. 3).

Subsequently, all 6220 expression profiles were clustered into 14 clusters. In this way two coordinates were assigned per gene, representing the expression profiles in both genotypes. All 196 (14×14) potential combinations are represented in FIG. 4 together with the absolute frequency of genes in each combination. Differences between clusters are indicated through a colour code.

The genes indicated in red (305) represent the genes for which wild-type gene expression is always higher than in solitary root. Genes of this kind, induced in wild-type and less in solitary root, are most likely involved in lateral root initiation. Therefore we focused on the genes, represented as cluster 1, 2, 3 and 4 in wild-type. In this way we could narrow down the total number of significantly regulated genes (3110) to 266 (˜9%) that might have a crucial role in lateral root initiation.

Example 3 Effect of Filtering on General Functional Categories

A comparison of the percentages of genes belonging to a functional category before and after clustering according to MATDB (MIPS Arabidopsis Thaliana DataBase) nicely illustrates the effectiveness of our cross-table based clustering (Table 1). The filtering procedure clearly resulted in an enrichment for genes related to cell cycle, RNA processing, DNA synthesis and signalling and development. These features confirm that the monitoring of cell cycle progression in the pericycle is possible using our lateral root inducible system. Furthermore, we found a higher percentage of genes involved in transcriptional regulation, indicating that there is a general need for increased transcriptional activity. The percentage of unclassified and unknown genes remains about at the same level. Moreover, a strong relative reduction of genes involved in stress, transport and metabolism was achieved through the applied selection criteria. Besides, the drop in the number of genes involved in transport might also be categorised as a drop in number of genes related to detoxification and stress responses.

Example 4 Cell Cycle Regulation During Lateral Root Initiation

Detailed examination of the selection reveals G1/S and S-phase markers such as Arath;CYCD3;2 and Arath;CYCA2;4. Furthermore, the link to S-phase entry/progression is never far-off as there is a high representation of genes involved in DNA replication and protein synthesis. This underlines the suitability of our approach to study auxin mediated cell cycle regulation. As interesting as core cell cycle events can be, they require upstream signalling cascades such as auxin signalling.

Example 5 Auxin Signalling During Lateral Root Initiation

In our stringent selection, several genes were detected belonging to gene families with known roles in auxin signalling such as Aux/IAAs, ARFs, ATGH3s and an ATSAUR (Hagen & Guilfoyle, 2000). Different mutants in genes belonging to the Aux/IAA gene-family have lateral root phenotypes (Fukaki et al., 2002; Park et al., 2002). Their gene-products act to repress the activity of ARF transcription factor dimers (Leyser, 2002).

Recently, researchers gained insight into the function of ATGH3-gene products, through the analysis of activation tagged lines (Takase et al., 2004). Several of these GH3 proteins have been shown to adenylate plant hormones and based on their substrate specificity and protein structure, they are subdivided into three major classes (Staswick et al., 2002). The members of group II, such as ATGH3-1, ATGH3-5 and ATGH3-6/DFL1, can adenylate IAA, negatively regulating auxin activity (Takase et al., 2004).

As for the Small Auxin Up RNAs (ATSAUR), very little is known about their function in auxin response. However, their auxin inducibility has been reported for several years (McClure & Guilfoyle, 1989).

Example 6 New Genes in Lateral Root Initiation: Transcription Factors

As transcription factors play central roles in patterning and development (Sabatini et al., 2003), it is obvious that such genes in our selection (19) will be of particular importance in the signalling cascades during lateral root initiation (Table 1). Most of the genes of this selection were recently be shown to be specifically expressed in stele tissue (including the pericycle) by a transcript profiling study of the Arabidopsis root tip (Birnbaum et al., 2003), justifying the selection criteria used in our study.

Interestingly, two of the AP2 domain transcription factors belong to the same subclade. This implicates that it is likely that these genes have redundant functions. Furthermore, there is one AP2 domain transcription factor that belongs to this same subclade of three genes, which is not represented on the microarray (Alonso et al., 2003). We hypothesize that this gene (At4g27950) could also be functionally redundant to the two other members of this subclade. Consequently, this gene was also added to our selection, bringing the final number of our selection to 20 genes.

Within our dataset there are 15 transcription factors for which no role in auxin signalling has been suggested. As for a start of validation it will be of our primary interest to do a functional analysis on these transcription factors with respect to lateral root initiation.

Many of these transcription factors have great potential for involvement in lateral root development, as they have homologues for which a role in organ development has been reported. The ABI3-gene was previously described as a seed-specific gene, but recently it has been shown to have a role in auxin signalling and lateral root development (Brady et al., 2003). Also, AP2 and several homeobox genes have been shown to be involved in floral organ development, which implies that homologs have great potential to be essential in the development of other organs such as lateral roots (Carpenter & Coen, 1990; Maes et al., 1999).

Interestingly, there is one transcription factor, MYB124, for which the mutant has an aberrant stomatal development. As the result of the mutation stomata with four guard cells are formed instead of two (Yang & Sack, 1995). Its upregulation upon auxin treatment of the root implicates that the MYB124 gene-product might have an as crucial role in the formative divisions in the pericycle (lateral root initiation) as it has in stomatal development.

Example 7 Identifying Asymmetric Cell Division Genes

Using LRI as a model to genetically dissect the asymmetric cell division, we investigated within our 10 clusters, which cluster contained the putative regulators of this type of division. First, we analyzed which cluster is strongly linked with the G2-to-M transition by verifying the expression profile during cell cycle progression using the genome wide expression data for synchronized Arabidopsis cell suspensions (Menges et al., 2003). We found that 48% of the genes in cluster 3 peaked at the G2-to-M transition. This is opposed to less than 10% of the genes that peaked at this transition in all the other clusters. This is a strong overrepresentation of G2-to-M related genes within this cluster as compared to the other clusters. Furthermore, this is 59% of the G2-to-M specific genes present in the whole dataset.

Secondly, we analyzed which cluster is potentially correlated with asymmetric cell division. For this we used protein sequences of genes that are assigned to the functional categories (godatabase.org/cqi-bin/amigo/go.cqi) “asymmetric cell division” and/or “establishment and/or maintenance of cell polarity” in a wide variety of organisms (i.e. Caenorhabditis elegans, Drosophila melanogaster, Schizosaccharomyces pombe, mouse . . . ). We performed a protein blast analysis with the protein sequences of various organisms and those Arabidopsis protein sequences of the genes assigned to our different clusters. This resulted in an overrepresentation of 54 genes putatively correlated with “asymmetric cell division”, “cell fate commitment” and/or “establishment and/or maintenance of cell polarity” in cluster 3.

To further analyze the process of asymmetric cell division, we therefore focused on the 340 genes within cluster 3. Within this cluster, 25% of the genes have been described; the remaining 75% is unknown, expressed, hypothetical or putative.

In order to even further reduce the number of interesting candidates; we subtracted those genes of which involvement in normal cell division (synchronised, dividing Arabidopsis cell suspension cells) is shown (Menges et al., 2003). After that analysis we ended up with 190 candidates potentially involved in asymmetric cell division.

Example 8 Meta-Analysis for Improvement of the Results

It has been previously demonstrated that cell cycle progression in the pericycle is not sufficient for SOLITARY ROOT/IAA14-mediated lateral root initiation in Arabidopsis thaliana (Vanneste et al., 2005).

To support this finding we analyzed the root phenotype of transgenic lines over-expressing (35S) cell cycle genes. Based on the cell cycle stage specific expression profiles upon lateral root induction shown in Himanen et al. (2002) and our dataset, we selected CYCD3;1 (G1-to-S and G2-to-M, Dewitte and Murray, 2003), E2Fa/DPa (G1-to-S, De Veylder et al., 2002) and CDKB1;1 (G2-to-M, Boudolf et al., 2004). The over-expression phenotype in 10 day old seedlings of all lines was analysed and compared to WT (FIG. 5). None of the transgenic lines showed a significant increase in the lateral root density. The double transgenic over-expression line of E2Fa/DPa even showed a strong decrease in the lateral root number compared to the wild type Col. Also over-expression of CDKB1;1 resulted in fewer lateral roots. Furthermore, in the case of CDKB1;1, the over-expression of dominant negative allele of CDKB1;1 (CDKB1;1.N161) (Boudolf et al., 2004), resulted in a stronger decrease of the number of lateral roots.

Next, we analyzed if auxin (NAA) application in combination with increased cell cycle gene expression could result in a higher number of lateral roots compared to the auxin or cell cycle alone. We therefore transferred 5 day old seedlings of the above mentioned transgenic Arabidopsis lines of E2Fa^(OE), DPa^(OEOE), CYCD3;1^(OE) and CDKB1;1^(OE) to increasing concentrations of auxin (10⁻⁸, 10⁻⁷ and 10⁻⁶ M of NAA) and analysed, after another 5 days of growth, their ability to initiate lateral roots. We found a significant increase in the CYCD3;1^(OE) opposed to the wild type. Similarly, the number of LRs/cm could be significantly increased in the E2Fa/DPa^(OE) transgenic line, until exceeding the wild type number at high auxin concentration. Even CDKB1;1^(OE) exceeded the WT number upon auxin application, while this was not the case for the CDKB1;1 DN^(OE) line.

The above results indicate that stimulating the basic cell cycle machinery is not sufficient for de novo lateral root initiation but when extra auxin is provided the enhanced cell cycle competence can be exploited to produce new organs. This corroborates the suggestion by Vanneste et al. (2005) that, next to cell cycle activation, another factor is required to specifically drive lateral root initiation.

Notwithstanding a putative function for CDKB1;1 in lateral root initiation, cell cycle genes are clearly not the key regulators for lateral root initiation. Hence, we searched within our dataset for potential specific regulators of lateral root initiation via meta-analysis. This meta-analysis was performed to reduce the number of genes from 1920 significant genes to 15 highly interesting candidates (Table 3). The analysis involved subsequent steps of overlapping and in depth analysis of subsets of genes as described below.

-   1) Affymetrix Arabidopsis ATH-1 Genome Array (22758 genes); -   2) Unique significantly differentially expressed genes (1920); -   3) Upregulated genes in asymmetric cell division during lateral root     initiation after selecting 1 cluster based on the following     criteria (340) using in depth analysis with functional categories     terms:     -   highest % G2-M genes     -   highest % genes involved in asymmetry     -   highest % genes involved in polarity     -   highest % genes involved in cell fate; -   4) Genes potentially involved in cell fate and cell polarity (190)     after subtracting the mitotic apparatus based on Menges et al.     (2003); -   5) Genes involved in auxin-induced cell fate and/or cell polarity in     the xylem pole pericycle during lateral root initiation (15) after     overlapping the remaining 190 genes with those lateral root     initiation genes (913) depending on rapid SLR/IAA14 degradation for     normal auxin responsiveness, as derived from the cross table (FIG.     4).

Example 9 BDL is Involved in Lateral Root Initiation

As was determined earlier (Vanneste et al., 2005), an important regulator mechanism for lateral root initiation is auxin signaling and transport. Table 4 lists the genes involved in those events and demonstrates that most of them are early up/down regulated. A number of genes have been shown to be involved in lateral root formation (ALF1/RTY/SUR1, Celenza et al., 1995, King et al., 1995, Boerjan et al., 1995; DFL1, Nakazawa et al., 2001) and for several Aux/IAAs and ARFs a role in lateral root initiation and/or formation was shown earlier (IAA19/MSG2, Tatematsu et al., 2004; ARF19, Wilmoth et al., 2004; IAA1/AXR5, Yang et al., 2004; IAA3/SHY2, Tian and Reed, 1999)

For BDL/IAA12, part of a pair of transcriptional regulators with MP/ARF5, we demonstrate the involvement in lateral root initiation. Xylem pole pericycle specific expression of a stabilised mutant version of the BDL protein in J0121xUAS:bdl (0.0±0.0) resulted in a lateral rootless phenotype, while the control lines Col-0 (3.0±0.1), J0121 (4.1±0.1), UAS:bdl (3.1±0.1), UAS:BDL (2.8±0.1) and J0121xUAS:BDL (3.6±0.1) displayed no reduction in the number of lateral roots/cm (FIG. 6).

Example 10 Role of CYCA2;4 in Lateral Root Formation

CYCA2;4 was identified as a putative important regulator of cell division during lateral root initiation (Vanneste et al., 2005). However, overexpression of CYCA2;4 did not induce an increase in lateral roots (similar to overexpression of other cell cycle genes), while it did stimulate cell cycle progression as exemplified by a strong reduction of endoreduplication level in cotyledons. Also in knock-outs we did not observe obvious changes in lateral root density. But, CYCA2;4 belongs to a small gene family consisting of 4 members. Combining mutations in various members of this family did result in dramatic reductions of lateral root density (FIG. 7). Taken together these data suggest that A2-type cyclins are required, but not sufficient for lateral root initiation to occur.

Interestingly, CYCA2;4—a core cell cycle gene—is retained in the list of genes after meta-analysis. Unfortunately, the lack of lateral root phenotype in the overexpression lines might suggest that a combination of genes/factors is required to specifically drive asymmetric cell division and lateral root initiation. The most likely candidates that, when combined, will induce lateral root initiation are within the subset of 15 genes identified under example 8.

Example 11 Mutant Screening

A number of SALK T-DNA insertion mutants from genes in various clusters were made homozygous and analyzed for their lateral root phenotype (FIG. 8). In the graph, bars of mutants with a significant increase or decrease in the lateral root number are colored green or red, respectively. The green or red box around part of the graph indicates genes from up- or down regulated dusters respectively.

In addition to the lateral root phenotype, defects in other processes requiring asymmetric cell divisions were also detected, i.e. stomata formation and embryogenesis.

Example 12 Expression Analysis by GUS/GFP Fusion

A number of promoter-GUS/GFP fusions from genes in various clusters were made homozygous and analyzed in detail for their expression pattern (FIG. 9). Mainly, the GFP expression pattern was in agreement with the up- or down regulation of the gene in micro array dataset.

For 4 genes the expression pattern was analyzed in detail, and revealed a specific up- or down regulation of the GUS/GFP in the lateral root initiation site at the time of asymmetric cell division in agreement with the transcript level detected in the micro array (FIG. 10).

Tables

TABLE 1 Overview of shifts in functional categories after filtering Functional categories % in 3110 % in 266 Cell cycle/DNA synthesis 3.9 8.3 Kinase/Phosphatase 5.5 3.4 Metabolism/Energy 22.7 12.4 Protein synthesis/degradation 9.7 9.0 RNA processing 1.6 3.0 Signalling/development 6.8 12.8 Stress 4.0 2.3 Transcriptional activity 10.9 14.7 Transport 7.2 2.3 Other 5.5 7.1 Unclassified/unknown 22.1 24.4 Lateral root initiation encrypted in ‘lateral root initiation’ genes

TABLE 2 List of up-regulated transcription factors that are not responsive in the mutant (slr) Cluster coordinates AGI code Description Wild-type Solitary root At2g33720 ABI3/VP1-related TF 4 7 At5g53290 AP2 domain TF 1 4 At4g23750 AP2 domain TF 1 6 At5g18560 AP2 domain TF 1 8 At1g28360 AP2 domain TF 2 8 At5g10510 AP2 domain TF 3 8 At5g57390 AP2 domain TF 3 8 At4g28640 Aux/IAA family (IAA11) 1 2 At4g32280 Aux/IAA family (IAA29) 1 2 At3g62100 Aux/IAA family (IAA30) 1 4 At5g43700 Aux/IAA family (IAA4) 2 5 At5g60450 Auxin Response Factor (ARF4) 2 8 At4g00940 DOF zinc finger protein 4 9 At1g27050 Homeobox TF 2 7 At2g01430 Homeobox TF 2 7 At1g14350 MYB domain TF (AtMYB124) 2 5 At1g18570 MYB domain TF (AtMYB51) 4 9 At2g47260 WRKY domain TF (WRKY23) 3 7 At5g26930 GATA zinc finger protein 1 4

TABLE 3 List of genes that are up-regulated in the xylem pole pericycle, that are not responsive in the slr mutant and that show potential involvement in the asymmetric cell division AGI code Description AT5G63950 SNF2 DOMAIN-CONTAINING PROTEIN/HELICASE DOMAIN-CONTAINING PROTEIN AT5G67100 DNA-DIRECTED DNA POLYMERASE ALPHA CATALYTIC SUBUNIT, PUTATIVE AT2G33620 DNA-BINDING FAMILY PROTEIN/AT-HOOK PROTEIN 1 (AHP1) AT2G46990 AUXIN-RESPONSIVE PROTEIN/INDOLEACETIC ACID-INDUCED PROTEIN 20 (IAA20) AT5G47440 EXPRESSED PROTEIN STRONG SIMILARITY TO UNKNOWN PROTEIN AT4G32460 EXPRESSED PROTEIN AT4G13210 PECTATE LYASE FAMILY PROTEIN AT3G59430 EXPRESSED PROTEIN AT3G01070 PLASTOCYANIN-LIKE DOMAIN-CONTAINING PROTEIN AT3G59420 RECEPTOR PROTEIN KINASE, PUTATIVE (ACR4) AT1G80370 CYCLIN, PUTATIVE AT1G69530 EXPANSIN, PUTATIVE (EXP1) AT4G02060 PROLIFERA PROTEIN (PRL)/DNA REPLICATION LICENSING FACTOR MCM7 (MCM7) AT5G67070 RAPID ALKALINIZATION FACTOR (RALF) FAMILY PROTEIN AT1G61580 60S RIBOSOMAL PROTEIN L3 (RPL3B)

TABLE 4 Expression in an early stage of genes involved in auxin signalling and transport as determined by the cell sorting approach. Gene Name(s) agi 0 h 2 h 6 h AAP6 At5g49630 0.852584 1.25473 1.549922 ALF1 SUR1 AT2g20610 3.526627 1.659614 1.132184 RTY ARF16 At4g30080 0.727253 1.044252 1.222434 ARF18 At3g61830 1.02858 1.042211 1.37744 ARF19 At1g19220 0.964729 2.260746 1.345781 AKF4 At5g60450 0.993832 2.433736 1.34132 ARF5 IAA24 At1g19850 0.805484 1.062866 1.646489 MP AtGH3_1 At2g14960 0.993763 14.46607 12.64589 AtGH3_4 At1g59500 1.832319 11.94342 5.062107 AtGH3_5 AT4g27260 4.933415 36.44469 28.02028 AtGH3_6 DFL1 At5g54510 4.107676 33.98892 33.18595 ATSAUR32 At2g46690 0.745399 1.083697 1.357203 ATSAUR51 At1g75580 0.641888 0.861681 1.394927 DFL1 AT5G54510 4.107676 33.98892 33.18595 HAT2 AT5G47370 3.866166 5.905478 1.72286 IAA1 AT4G14560 1.444098 2.650251 3.498028 IAA11 AT4G28640 1.081115 2.990535 3.893183 IAA12 BDL AT1G04550 1.070244 2.483024 2.141742 IAA13 AT2G33310 3.386846 16.95071 17.15706 IAA17 AXR3 AT1G04250 4.20049 5.560019 1.337217 IAA19 MSG2 AT3G15540 8.950041 37.74343 43.64249 IAA20 AT2G46990 0.643367 0.719127 1.830361 IAA26 PAP1 AT3G16500 1.67767 4.3412 2.185664 IAA29 AT4G32280 1.230655 15.62485 12.44667 IAA3 SHY2 AT1G04240 0.649735 1.143323 0.778411 IAA5 AT1G15580 0.800279 1.701733 2.127686 PINOID-LIKE At3g44610 0.730512 0.863134 2.114596

TABLE 5 code of the mutants used Code on graph AGI H19 At3g59850 R At4g23750 S At4g23750 H12 At4g38210 H10 At5g15080 101 At5g51560 KO1 At1g11140 KO2 At1g55580  90 At1g57820 H8 At1g69530 108 At1g72250 H2 At2g06850  67 At2g22610  68 At2g28620  94 At2g28620 135 AT2G33620  59 At3g51280  87 At3g51740 H14 At3g53190  83 At4g02150  85 At4g05190 105 At4g18570  91 At4g21820 H11 At4g29360  75 At4g32830 106 At4g32830 H23 At5g08000  88 At5g45780 124 At5g47440  65 At5g48460 H3a At1g08840 M At1g72310 H4 At1g75640  5 At4g28640 H15 At1g64390 H20 At3g58040 H21 At1g14720 H6 At1g29050 H29 At1g53500 H1 At1g60610 H26 At2g45470 H28 At3g10810 H5 At3g52370 H18 At4g03960 H9 At4g14130 H27 At5g18650 H13 At5g38895 H17 At5g54160 H16 At5g57740 H22 At5g03650  6 At2g33830  22 At2g33830 H7 At3g54920 H24 At3g54920

TABLE 6 genes used in GUS fusion 1 pAt4g13770::GUS 2 pAT2G20610::GUS 3 pAt3g46130::GUS 4 pAt3g11280::GUS 5 pAt1g49740::GUS 6 pAt1g64405::GUS 7 pAt3g55620::GUS 8 pCYCB1;1::GUS 9 pCYCB1;3::GUS 10 pCYCB2;2::GUS 11 pCYCB2;4::GUS 12 pCYCA2;2::GUS 13 pCYCA2;3::GUS 14 pCDKB2;2::GUS 15 pCKS2::GUS 16 pDEL3::GUS 17 pAt3g58100::GUS 18 pAt1g54990::GUS 19 pAt5g26930::GUS 20 pAT1G69530::GUS 21 pACR4>>H2B::YFP (Gifford et al., 2003) References

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The invention claimed is:
 1. A method to identify a nucleic acid molecule encoding a protein involved in asymmetric cell division, comprising: (1) subjecting roots of a wild type plant to a treatment inducing lateral root initiation in a synchronous way; (2) subjecting roots of a mutant not developing lateral roots by a defect in auxin signalling to a treatment inducing lateral root initiation in the wild type in a synchronous way; (3) identifying a nucleic acid molecule that is expressed at a higher level in the wild type plant xylem pole pericycle cells than in the mutant plant xylem pole pericycle cells using microarray analysis comprising: (i) isolating xylem pole pericycle cells of the lateral root inducing portion of the wild type roots by using a xylem pole pericycle specific GFP marker line followed by cell sorting; (ii) isolating xylem pole pericycle cells of the lateral root inducing portion of the mutant roots by using a xylem pole pericycle specific GFP marker line followed by cell sorting; (iii) from the wild type and mutant xylem pole pericycle cells, generating nucleic acids that are used in microarray analysis and; (iv) identifying a nucleic acid molecule using microarray analysis that is expressed differentially between the wild type and mutant xylem pole pericycle cells, wherein said nucleic acid molecule encodes a protein involved in asymmetric cell division comprising the sequence of SEQ ID NO: 4, or wherein said nucleic acid molecule encodes a protein involved in asymmetric cell division comprising a sequence having at least 95% sequence identity to SEQ ID NO:
 4. 2. The method according to claim 1, whereby said mutant is slr-1.
 3. The method of claim 1, wherein the xylem pole pericycle marker line is the GFP marker line J0121.
 4. A method of modulating early lateral root initiation, the method comprising introducing into a plant or plant cell an isolated nucleic acid molecule encoding the protein of SEQ ID NO: 4 or encoding a protein having at least 95% sequence identity to SEQ ID NO: 4, wherein the nucleic acid molecule is operably linked to a heterologous promoter; and selecting a plant or plant cell for modified early lateral root initiation relative to a corresponding wild type plant or plant cell.
 5. The method of claim 4, wherein the nucleic acid encodes a transcription factor.
 6. A method of modulating lateral root formation comprising introducing into a plant or plant cell an expression construct for modulating asymmetric cell division comprising a nucleic acid molecule encoding a protein involved in asymmetric cell division comprising the protein of SEQ ID NO: 4 or comprising a protein having at least 95% sequence identity to SEQ ID NO: 4, wherein the nucleic acid molecule is operably linked to a heterologous promoter; and selecting a plant or plant cell for modified lateral root formation relative to a corresponding wild type plant or plant cell.
 7. A method for production of a plant having modified early lateral root initiation, comprising introducing into a plant or plant cell an isolated nucleic acid molecule encoding the protein of SEQ ID NO: 4 or encoding a protein having at least 95% sequence identity to SEQ ID NO: 4, wherein the nucleic acid molecule is operably linked to a heterologous promoter; and selecting a plant for modified early lateral root initiation relative to a corresponding wild type plant.
 8. The method of claim 4, wherein the nucleic acid encodes a protein comprising the sequence of SEQ ID NO:
 4. 9. The method of claim 4, wherein the nucleic acid encodes a protein comprising a sequence having at least 97% sequence identity to SEQ ID NO:
 4. 10. The method of claim 7, wherein the nucleic acid encodes a protein comprising the sequence of SEQ ID NO:
 4. 11. The method of claim 7, wherein the nucleic acid encodes a protein comprising a sequence having at least 97% sequence identity to SEQ ID NO:
 4. 